Nov. 9, 2022, 2:20 a.m. | Gonzalo Munilla Garrido, Xiaoyuan Liu, Florian Matthes, Dawn Song

cs.CR updates on arXiv.org arxiv.org

Since its introduction in 2006, differential privacy has emerged as a
predominant statistical tool for quantifying data privacy in academic works.
Yet despite the plethora of research and open-source utilities that have
accompanied its rise, with limited exceptions, differential privacy has failed
to achieve widespread adoption in the enterprise domain. Our study aims to shed
light on the fundamental causes underlying this academic-industrial utilization
gap through detailed interviews of 24 privacy practitioners across 9 major
companies. We analyze the results …

differential privacy industry lessons learned privacy

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